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A Review of Decision Support Systems for Manufacturing
Systems
∗
Andreas Felsberger
University of Klagenfurt
Operations-, Energy-, and
Environmental Management
Universitätsstrasse 65-67
Klagenfurt, Austria
[email protected]
†
Bernhard Oberegger
University of Klagenfurt
Operations-, Energy-, and
Environmental Management
Universitätsstrasse 65-67
Klagenfurt, Austria
[email protected]
‡
Gerald Reiner
University of Klagenfurt
Operations-, Energy-, and
Environmental Management
Universitätsstrasse 65-67
Klagenfurt, Austria
[email protected]
ABSTRACT
1.
In the field of manufacturing systems automated data acquisition and development of technological innovations like
manufacturing execution systems (MES), Enterprise Resource
Planning (ERP), Advanced Planning Systems (APS) and
new trends in Big Data and Business Intelligence (BI) have
given rise to new applications and methods of existing decision support technologies. Today manufacturers need an
adaptive system that helps to react and adapt to the constantly changing business environment. The internal data
processing system of a company can only offer minimum support because it is related to transactions. In this case, decision support systems (DSS) combine human skills with the
capabilities of computers to provide efficient management
of data, reporting, analytics, modeling and planning issues.
DSS provide a distinction between structured, semi structured and unstructured data. In particular, a DSS reduces
the quantity of data to a high quality structured amount;
due to this, decisions are made to support the manufacturing process. In addition, the goal of these systems is to
avoid problems within the production process even before
they emerge. This paper gives an overview of the state-ofthe-art literature on DSS and describes current techniques
of relevant DSS applications within manufacturing environments.
Decision support systems (DSS) are about developing and
deploying IT-based systems to support decision processes [5,
30]. Since the 1970s researchers,have focused on developing
computer technology based solutions that can be used to
support complex decision-making and problem solving [31].
The area of the information systems and the technologies
have evolved significantly in the last decades. Nowadays,
the tendency towards business intelligence (BI) is one of the
key drivers for emerging developments in DSS technologies
and methods. Overall, a DSS is an interactive computer information system that solves the problem of non-structures
and can help decision makers to use data and models [86].
Suri and Whitney showed already in 1984 [78] that especially in flexible manufacturing systems, DSS are installed
to improve productivity. Moreover, traditional manufacturing systems are transforming into knowledge-based manufacturing environments based on digitization, computer networks, artificial intelligence and rapid response across supply
chains. These aspects are fundamental requirements for today’s manufacturing systems to satisfy costumer needs and
to produce high quality products within small batch sizes
and sustainable consciousness. The way to a fitting DSS
is quite marbled. First, the provided data, determined by
several data gathering systems like ERP has to be filtered.
After conditioning the data, several alternatives can be set
to solve a defined problem and make at least a choice of
decision design and implement the solution. This literature
review focuses on past and current research on decision support systems in manufacturing. The paper is organized as
follows: Section 2 gives a general overview and definition
about decision support systems, in particular this part describes why DSS are used, how they are used and shows
the structural components of the decision-making process.
Section 3 describes upcoming trends of automated decisionmaking processes and new types of intelligent decision support systems (IDSS) in the context of Business Intelligence
and Cyber-Physical Production Systems (CPPS). Section 3
concludes the paper and gives an outlook on further research
questions.
Keywords
Decision Support Systems, Manufacturing, P&OM
∗Andreas Felsberger, Research Assistant at OEE, University
of Klagenfurt, Austria
†Bernhard Oberegger, Research Assistant at OEE, University of Klagenfurt, Austria
‡Gerald Reiner, Head of Department OEE, University of
Klagenfurt, Austria
SamI40 workshop at i-KNOW ’16 October 18–19, 2016, Graz, Austria
c 2016 for this paper by its authors. Copying permitted for
Copyright private and academic purposes.
INTRODUCTION
LITERATURE REVIEW ON DECISION
SUPPORT SYSTEMS
Decision Support Systems Definitions and
Types
Choice Phase
A DSS is defined as a computer system that deals with
a problem where at least some stage is semi-structured [72,
43] or unstructured [10, 77]. Simon 1960 described decision
problems as existing on a continuum from programmed (routine, well-structured and easily solved) to non-programmed
(new, ill-structured and difficult to solve). [73, 16] Keen 1980
defined a DSS as a concept of the role of computers within a
decision making process. Defined in terms of the structure of
the task, it addresses the requirement of a distinctive design
strategy which supports the cognitive processes of individual
decision makers and reflects an implementation strategy for
making computers useful to managers [43]. Haettenschwiler
[26] differentiates between passive, active and cooperative
DSS,
1
6
Problem &
Requirement
Definition
Implementation
2
5
Alternative
Generation
Choice of
Decision Design
4
Intelligence Phase
2.1
Problem
Recognition
Implementation
Phase
2.
3
Alternative
Analysis
Model
Development
Design Phase
Figure 1: The decision-making process1
bottom process into a closed loop variation.
• A passive DSS is a system that supports the process
of decision making, but that cannot produce decision
suggestions or solutions.
• An active DSS can generate such decision suggestions
or solutions.
• A cooperative DSS permits the decision maker to modify, complete, or refine the decision recommendations
provided by the system, before sending them back to
the system for validation [25]. The system again improves, adds, and refines the suggestions of the decision
maker and sends them back for validation. [41]
Sprague and Carlson 1982 defined DSS as a ”class of information system that draws on transaction processing systems
and interacts with the other parts of the overall information system to support the decision-making activities of managers and other knowledge workers in organisations”.[77, 63]
Power 2002 defines DSS as interactive computer-based systems that help people use computer communications, data,
documents, knowledge, and models to solve problems and
make decisions [62]. In addition, Power distinguishes between enterprise-wide DSS and desktop DSS. An enterprisewide DSS is connected to a large data warehouse and is
used by many managers in the company. A desktop DSS is
a small system that runs on a single PC [60].
2.2
Decision Making Process
The decision-making process was continuously developed
over several years amongst others by Simon [73], Harris [29]
and Shim [72]. The process is determined as an identification of alternatives as possible solutions for an upcoming
ill-structured problem. In this case, the aim is not to identify as many of these alternatives as possible, but rather the
one that fits best with the interpreted goals, objectives and
values. The decision-making process itself goes through multiple phases. First, it is necessary to identify a problem and
to determine who are the decision-makers and stakeholders
in the decision process. The original model shows a top to
bottom process flow but the current status may always return to a previous step from any point in the process [7].
Figure 1 of this paper shows that we broke down the top to
Steps 1 and 2 - Problem Requirement Definition and
Alternative Generation: Once the problem has been recognized, the first step in the decision-making process is the
problem definition [72]. Step 1 aims to express the issue in a
statement that describes the initial and desired conditions.
The second part of this step is to determine requirements
to obtain a list of absolute exigencies and goals. The requirements define how the solution to the problem acts [7,
57]. Step 2, Alternative Generation, deals with the creation
of possible alternative solutions for decision making uncertainties. One key aspect is that the alternatives must meet
the determined requirements. This phase of the decisionmaking process is also called the ”Intelligence Phase” which
consists of finding, identifying and formulating the problem
or situation. It gives an overview about the current status
of an identified problem. The result of the intelligence phase
should be a decision statement [73].
Steps 3 and 4 - Model Development and Alternative Analysis: After the alternative generation and selection of fitting
alternatives the next step is the model development. Models
are developed to analyze the various alternatives, simultaneously criteria should be defined to measure the effectiveness
of the alternative. The best model to compare alternatives is
that which will achieve the selected goals and criteria soonest. Once the model is developed, the next step will be
the alternative analysis which concludes the evaluation of
alternatives against criteria, selection of a decision making
tool e.g. Pros and Cons Analysis, Kepner-Tregoe Decision
Analysis (K-T), Analytical Hierarchy Process (AHP), MultiAttribute Utility Theory Analysis (MAUT) or Multi Criteria Decision Analysis (MCDA) [44, 56, 18, 52, 22]. However, ongoing research aims to find out which method is
more appropriate for which type of problem, to differ between advantages and disadvantages of using one method
over another and if there is a decision change due to using
different methods [83]. Finally, there is also the validation
of solutions against the problem statement which aims to
ensure that it truly solves the problem identified. As shown
in figure 1, this phase is referred to as the so called ”Design
Phase” [73], where alternatives are developed and objectives
1
Shim et al. (2002) and Simon (1960), modified by authors.
become linked to the decision-model.
The final steps are 5 and 6 - Choice of Decision Design
and Implementation: These steps are taken as a basis of the
”Choice Phase” [73] of the model. This phase is about the
selection of the developed alternatives of the design phase.
The final product of the process is a decision or model that
is implementable into the decision environment, rolled-out
in the so called implementation phase. The solution should
satisfy the desired state, meet requirements and best achieve
the goals within the values of the decision process. [7, 72]
2.3
The Architecture of DSS
A DSS has several fundamental components. The database
management system (DBMS) is a software package, which
is responsible for data access and manipulates and manages
internal as well as external stored data in databases. Second, the model management system (MBMS) uses various
kinds of mathematical and analytical models or simulations
to represent and analyze complex data and the user interface component which handles the interaction of the user
with the system [79]. Figure 2 gives a conceptual overview
of the architecture of a DSS and the components mentioned
above.
Other computerbased systems
External/internal
data
Model
management
Data management
Knowledge
management
that provide the functionality. Analytic tools based on algebraic models allocate an elementary level of functionality.
They are used many times in building model-driven DSS applications. Generally algebraic models are developed within
spreadsheets. To develop and build more complex models for
decision making, decision analysis, optimization and mathematical programming models, and simulation techniques
were used.
In general, models in a model-driven DSS represent a simplification of the reality. Decision analysis models refer to
statistical tools and methods such as analytical hierarchy
process (AHP), decision tree analysis [59], multi-criteria decision analysis [35, 70] , and probabilistic forecasting [28,
48]. The target of a decision analysis is to discover the most
favorable alternative under the given situation. Optimization models integrated into a DSS have been developed in
many environments. Especially in production & operations
management and supply chain management the area of optimization models has become an important field for DSS [11,
50, 76]. Today, various decision support models are available
for different levels of the supply chain, including production
planning and scheduling [23, 53, 82], demand management
[71, 38], and logistics planning [80, 36]. Model-driven DSS
using simulation and rapid modeling [68, 69] techniques conduct multiple experiments to show the effects of alternative
conditions and courses of action [34]. For decision support
in the area of supply chain management several kinds of
simulation methods are in common use, e.g. Monte Carlo
simulation, discrete simulation, agent-based and multi-agent
simulation, system dynamics, and visual simulation [49, 66,
84].
2.4.2
User interface
Decision maker
Figure 2: Schematic view of DSS components2
Subsequent to this architecture the literature names five
relevant applications of DSS. Model-driven DSS, data-driven
DSS, communication-driven DSS, document-driven DSS and
knowledge-driven DSS. These applications consist of diverse
sub technologies and decision support techniques for decision
making.
2.4
2.4.1
DSS Application Development
Model-driven DSS
A model-driven DSS uses algebraic decision analytic, financial, optimization, and simulation models for decision
support [65]. A model-driven DSS is designed for the manipulation of model parameters by the user and to support
decision makers in analyzing a given situation. Usually they
are not data intensive. There is no need of large databases
for model-driven DSS, but for specific analyses they may
need to be extracted. The main component in the modeldriven DSS architecture are one or more quantitative models
2
Turban et al. (2005), modified by authors.
Data-driven DSS
A data-driven DSS enables access to and manipulation
of structured data and can handle time-series of internal
as well as external company data and real-time data [61].
Data-driven DSS are separated by functionality. While simple file systems accessed by query and retrieval tools facilitate an elementary level of functionality, management reporting systems like Data Warehouses or Executive Information Systems (EIS) which allow the manipulation of data
by computerized tools provide more in-depth functionality.
Another example of data-driven DSS are Business Intelligence Systems or Online Analytical Processing (OLAP) [63]
which provide the highest mode of operation and decision
support [21]. Business Intelligence Systems help organizations to make decisions by using technology for reporting and
data access and analytical practices. In general, BI systems
help to formulate decisions by triggering, manipulating and
analyzing data or information stored in historic databases
[33]. The main goal of Business Intelligence and Analytic
Systems is to increase the quality of the information that is
available for decision making induced by the improvement of
data processing [15, 58]. In this case, the key requirements
of a typical data-driven DSS are access to a large amount of
data and, at the same time, high quality of the underlying
data. The success of a DSS always depends on the access to
accurate, well-structured and organized data [64]. If these
requirements are not fulfilled, a data-driven DSS will not
work efficiently.
2.4.3
Communication-driven DSS
Typically, a communication-driven DSS relies on hybrid
network and electronic communication technologies to connect decision-makers and to create an environment of resource and information sharing, collaboration and communication among a group of decision makers [9]. One big subcategory of decision-making, developed over several years of
research in this field, is group decision making [19], later
extended into so-called Group Decision Support Systems
(GDSS) [19, 20]. These systems include problem-structuring
techniques, like planning and modeling tools. At this point
the link between the single DSS applications is obvious. Another idea of communication-driven DSS are Collaborative
Decision Support Systems (CDSS) [40, 42] featured as interactive computer-based systems where a group of decision
makers works as a team to find solutions and alternatives
for recognized, new and ill-structured problems [46]. Multiple IT technologies with integrated modeling and analysis
tools accomplish a user-friendly environment for exploring
solutions and making decisions [74].
2.4.4
Document-driven DSS
Over the years document management has become more
and more important to companies. Consequently, the amount
of documents, correspondence, images, sounds, videos and
hypertext documents stored in some kind of system ever increasing. Today there are existing huge document databases
and the world wide web technologies increased the development of document-driven DSS [17]. Examples for decisionaiding tools are search engines linked to document-driven
DSS like WebCrawler or Alta Vista. Mostly, documents are
not standardized in a uniform pattern. In this way information retrieval is used to find documents out of an unstructured form within large data collections. Information
retrieval systems like Lexis-Nexis or InfoSys give documents
a structure for better decision making support.The use of
text based information retrieval systems can help to locate
relevant documents and mine the organization data [75].
2.4.5
Knowledge-driven DSS
Knowledge-driven DSS have their origin in Intelligent Decision Support Systems or in a broader sense, in Artificial Intelligence (AI)[54, 55]. Knowledge-driven DSS are
computer-based reasoning systems with the distinction that
AI technologies, management expert systems, data mining
[61] technologies and communication mechanisms are integrated. Intelligent DSS are divided into two evolutionary
developments. The first types of these systems were about
rule-based expert systems [14] widely used for scheduling
in production systems [63]. Expert systems are based on
the use of heuristics, which can be understood as strategies
that lead to the correct solution for a problem. For these
systems it is always necessary to use human expert knowledge collected in a database, to solve problems [2]. The
second generation uses neural networks [39], fuzzy logic and
genetic algorithms [4], which are similar to linear programming models and conduct random experiments by selecting
variables without identified values to find the fitness function. Genetic algorithm systems combine genes to generate
optimized value and have been applied to combinatorial and
discrete parameter optimization problems [67]. Knowledgebased DSS therefore aims to identify specific knowledge and
a variety of data mining tools and technologies.
Nowadays companies try to automate more and more of
their processes. One of these automated processes should
include data management that is crucial for decision support in a facility. Technological drivers have to be leveraged
in order to process data effectively [81]. Decision support
systems require filtered, structured data already fed into in
a database. Therefore data must be mined and turned into
knowledge. The next steps show the process of data mining:
[24, 27, 47]
• Data cleaning and definition of mining goals
In the first step inconsistent and erroneous data is filtered out and mining goals are established.
• Data integration
Data arises from different data sources, this step integrates and combines various sources.
• Data selection
Selection of useful data which is relevant for the analysis.
• Data transformation
Data exists in multiple forms, in this phase data is
transformed into a suitable basis for the mining process.
• Data mining
The 5th step is the proper key process where mining
functions and algorithms are applied to extract data
samples for knowledge discovery.
• Evaluation of results
The mined results should fit the mining goals established in the first step of the process. The advantage
of the extracted knowledge is now measurable and suitable for further manipulation.
• Knowledge deployment
The final phase is to make decisions based on the mined
results.
For the purpose of these applications it is crucial to set up a
well designed DSS user interface. The interface or dashboard
influences how the user views results of a mining process and
hence influences choices [65].
2.5
Trends and further research in the field of
decision support
Intelligent decision making is one of the current keywords
in this research field and is interrelated to modern business
development. The potential of big data and advanced artificial intelligence offers new insights for innovations on DSS
and for decision making in the form of more objective and
evidence-based smart decisions [1, 87]. The main impact
and key aspect of these intelligent systems is an improved
method of data analysis. The collection, storage and unregulated use of data has no impact on decision making
[6]. Corresponding to the main dimensions named the Big
Data ”Vs” - ”Volume” (data is generated in large amounts),
”Variety” (data is generated in different sources and types,
structured and unstructured), ”Velocity” (data is generated,
captured and stored in real-time and continuously), ”Veracity” (data is erroneous and inconsistent), ”Validity” (ensures
that the data is measured in the right way and fits criteria) and ”Value” (how data and information is applied or
• Documentation-driven DSS are used to give documents
a structure for better decision making support. Information retrieval systems or search engines (decisionaiding tools) linked to databases are types of these
systems.
implemented into the business environment), decisions are
made by considering these aspects [8, 37, 51, 85]. A decision making-process is only successful when it achieves all of
these characteristics in form of a basis of well-structured and
analyzed data. With reference to the mentioned DSS applications in this paper, DSS research and development will
benefit from progress in huge data bases, AI and humanmachine interactions [63],
• Knowledge-driven approaches are computer-based reasoning systems with the distinction that AI technologies, management expert systems, data mining technologies and communication mechanisms are integrated.
• Data-driven DSS will use real-time access to larger and
better integrated databases.
• Communication-driven approaches rely on hybrid network and electronic communication technologies to connect decision-makers and to create an environment of
resource and information sharing, collaboration and
communication among a group of decision makers.
• The complexity and realism of model-driven DSS will
increase significantly.
• Communication-driven DSS will have more real-time
video communication support and the research on collaborative decision support systems reaches a new era
and finally knowledge-driven DSS are becoming more
and more rich in content.
In the case of the Industry 4.0 paradigm, the massive increase of data allows the optimization and improvement of
models to enhance error analysis and the prediction of specific situations to set up counteractive measures. Decisions
made to optimize efficiency and effectiveness of manufacturing systems are reaching from the strategic level to tactical and operational production scheduling and control. Automating these decisions by innovative algorithms and intelligent software applications based on the knowledge in the
field of production and operations management, the performance of a manufacturing system can be maximized. In
order to solve such complex decision making and control
problems in practice, usually a structuring of the object
into sub-areas is carried out, so that finally a network of
agents is realized, which is divided both horizontally and
vertically [3]. Such agents may work largely autonomously
based on their models and knowledge bases or cooperatively
with human decision-makers in the form of recommender
systems [12, 13]. To achieve these objectives the improvement of the analysis, diagnosis, prognosis and quality of
the decisions of cooperative agents by methods from the
fields of model-based reasoning, automated decision-making,
machine learning, diagnosis, knowledge-based configuration,
planning and scheduling, recommender systems are necessary.
3.
CONCLUSION
This paper provides a review of research on DSS. The review of literature has shown that Decision Support Systems
offer a variety of application possibilities. Power [63] identified five specialized types of DSS, including data-driven,
model-driven, documentation-driven, knowledge-driven and
communication-driven DSS,
• Data-driven concepts require the availability of a lot of
structured data and can handle time-series of internal
as well as external company data and real-time data.
• Model-driven approaches are designed to allow the manipulation of model parameters by the user and to support decision makers in analyzing a given situation.
Model-driven DSS are not data intensive.
Another aspect to come in for criticism is that most results of data processing and decision making are based on
mean values as a basis of expectancy values. Expected values are commonly used as justification of decisions and their
underlying criteria. If so, it is necessary to distinguish the
stochastic properties of the expectation value. Discrepancies
between de facto and theoretic decision behavior mostly depend on an erroneous interpretation of expected values [45].
The question that arises is how can new technologies be
used to enhance data collection? But more than data collection, how can it be used to enhance data processing and
evaluation to improve decision making and behaviour in the
future? [32]
4.
ACKNOWLEDGEMENT
A part of the work has been carried out within the scope
of the project Power Semiconductor and Electronics Manufacturing 4.0 - (Semi40), under grant agreement No 962466.
The project is cofunded by grants from Austria, Germany,
Italy, France, Portugal and - Electronic Component Systems
for European Leadership Joint Undertaking (ECSEL JU).
5.
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